Articles | Volume 9, issue 8
https://doi.org/10.5194/amt-9-4123-2016
https://doi.org/10.5194/amt-9-4123-2016
Research article
 | 
29 Aug 2016
Research article |  | 29 Aug 2016

Errors in radial velocity variance from Doppler wind lidar

H. Wang, R. J. Barthelmie, P. Doubrawa, and S. C. Pryor

Related authors

Lidar arc scan uncertainty reduction through scanning geometry optimization
Hui Wang, Rebecca J. Barthelmie, Sara C. Pryor, and Gareth. Brown
Atmos. Meas. Tech., 9, 1653–1669, https://doi.org/10.5194/amt-9-1653-2016,https://doi.org/10.5194/amt-9-1653-2016, 2016

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
The High lAtitude sNowfall Detection and Estimation aLgorithm for ATMS (HANDEL-ATMS): a new algorithm for snowfall retrieval at high latitudes
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi
Atmos. Meas. Tech., 17, 2195–2217, https://doi.org/10.5194/amt-17-2195-2024,https://doi.org/10.5194/amt-17-2195-2024, 2024
Short summary
Next-generation radiance unfiltering process for the Clouds and the Earth's Radiant Energy System instrument
Lusheng Liang, Wenying Su, Sergio Sejas, Zachary Eitzen, and Norman G. Loeb
Atmos. Meas. Tech., 17, 2147–2163, https://doi.org/10.5194/amt-17-2147-2024,https://doi.org/10.5194/amt-17-2147-2024, 2024
Short summary
Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data
Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala
Atmos. Meas. Tech., 17, 2165–2182, https://doi.org/10.5194/amt-17-2165-2024,https://doi.org/10.5194/amt-17-2165-2024, 2024
Short summary
A directional surface reflectance climatology determined from TROPOMI observations
Lieuwe G. Tilstra, Martin de Graaf, Victor J. H. Trees, Pavel Litvinov, Oleg Dubovik, and Piet Stammes
Atmos. Meas. Tech., 17, 2235–2256, https://doi.org/10.5194/amt-17-2235-2024,https://doi.org/10.5194/amt-17-2235-2024, 2024
Short summary
Investigation of gravity waves using measurements from a sodium temperature/wind lidar operated in multi-direction mode
Bing Cao and Alan Z. Liu
Atmos. Meas. Tech., 17, 2123–2146, https://doi.org/10.5194/amt-17-2123-2024,https://doi.org/10.5194/amt-17-2123-2024, 2024
Short summary

Cited articles

Banta, R. M., Pichugina, Y. L., Kelley, N. D., Hardesty, R. M., and Brewer, W. A.: Wind energy meteorology: Insight into wind properties in the turbine-rotor layer of the atmosphere from high-resolution Doppler lidar, B. Am. Meteorol. Soc., 94, 883–902, https://doi.org/10.1175/BAMS-D-11-00057.1, 2013.
Barthelmie, R. J., Wang, H., Doubrawa, P., Giroux, G., and Pryor, S. C.: Effects of an escarpment on flow parameters of relevance to wind turbines, Wind Energy, https://doi.org/10.1002/we.1980, online first, 2016.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M.: Time Series Analysis: Forecasting and Control, John Wiley & Sons, 712 pp., 2015.
Branlard, E., Pedersen, A. T., Mann, J., Angelou, N., Fischer, A., Mikkelsen, T., Harris, M., Slinger, C., and Montes, B. F.: Retrieving wind statistics from average spectrum of continuous-wave lidar, Atmos. Meas. Tech., 6, 1673–1683, https://doi.org/10.5194/amt-6-1673-2013, 2013.
Burton, T., Sharpe, D., Jenkins, N., and Bossanyi, E.: Wind energy handbook, John Wiley & Sons, 780 pp., 2011.
Download
Short summary
This paper investigates how long a sampling duration of lidar measurements should be in order to accurately estimate radial velocity variance to obtain turbulence statistics. Using observations and statistical simulations, it is demonstrated that large probe volumes in lidar measurements increase the autocorrelation values, and consequently the uncertainty in radial velocity variance estimates. It is further shown that the random error can exceed 10 % for 30–60 min sampling duration.